How does text classification reduce manpower costs?
There are several advantages:


Some text classification systems are much more accurate than 50%. For example, most spam classification systems are 99.9% accurate, or more. There will be little value to having employees review these labels.
Many text classification systems can output a confidence as well as a label. You can selectively have employees review only the examples the model is not confident about. Often these will be small in number.
You can usually test a text classification model by having it classify some unseen data, and then asking people to check the work. If you do this for a small number of examples, you can make sure the system is working. You can then confidently use the system on a much large set of unlabeled examples, and be reasonably sure about how accurate it is. 
For text, it is also important to measure how much different people agree on the ratings. You are unlikely to do better than this, because this gives you a notion of the subjectivity of the specific problem you are working on. If people disagree 50% of the time anyway, maybe you can accept a 50% failure rate from the automated system, and not bother checking its work.